Evidence Of Nonlinear Dependence Research Articles

Adaptive market hypothesis: An empirical analysis of the Wine Market

We test the nature of weak form informational efficiency present in the wine market using daily return of LIV-EX 50 index from 1/1/2010 to 12/6/2020. First, we employ a number of statistical tests including variance ratio tests, tests for linear and non-linear dependence and Hurst coefficient. The tests are applied on the full dataset and on four non overlapping sub-samples of equal length. The variance ratio tests provide a mixed regarding informational efficiency. Evidence of non-linear dependence in the return series was found. The Hurst coefficient values confirm the presence of long run persistence in the wine market. Based on the mixed evidence, we test the possibility of adaptive nature of the wine market. We employ the newly proposed Adaptive Index (AI) to quantify the degree of information inefficiency in the wine market at any instance. Our results confirm that wine market is adaptive and periodically shifts between states of efficiency and inefficiency. The wine market is found to be relatively free from the Covid-19 induced shock and the safe haven property of wine is thus confirmed. Finally, impact of various macroeconomic and financial events on wine market efficiency is identified by using AI.

Dynamic structure of the spot price of crude oil: does time aggregation matter?

This paper assesses nonlinear structures in the time series data generating mechanism of crude oil prices. We apply well-known univariate tests for nonlinearity, with distinct power functions over alternatives, but with different null hypotheses reflecting the existence of different concepts of linearity and nonlinearity in the time series literature. We utilize daily data on crude oil spot price for over 26years, as well as monthly data on crude oil spot price for 41years. Investigating the monthly price of crude oil along with the daily price distinguishes the approach of this paper from existing studies focusing on the time series structure of crude oil price. All the tests detect strong evidence of general nonlinear serial dependence, as well as nonlinearity in the mean, variance, and skewness functions in the daily spot price of crude oil. Since evidence of nonlinear dependence is less dramatic in monthly observations, nonlinear serial dependence is moderated by time aggregation in crude oil prices but not significantly.

Testing for Nonlinear Dependence in the Credit Default Swap Market

The objective of this paper is to test for nonlinear dependence in the GARCH residuals of a number of asset classes using nonlinear dynamic tools. The equity and bond market samples appear to be independent once GARCH has been applied, but evidence of nonlinear dependence in the CDS GARCH residuals is found. The sensitivity of this result is analysed by changing the specifications of the GARCH model, and the robustness of the result is verified by applying additional tests of nonlinearity. Evidence of nonlinear dependence in the GARCH residuals of CDS contracts has implications for the accurate modeling of the marginal distribution of the CDS market, for pricing of CDS contracts, for estimating risk neutral default probabilities in the bond market, as well as for bond market hedging strategies.

Nonlinearities in the black market zloty-dollar exchange rate: some further evidence

This study reappraises the evidence for nonlinear dependence in the monthly black market exchange returns of the Polish zloty, 1955–1990. Predictive asymmetry is reported in conditional variance such that depreciatory shocks have a greater impact on subsequent volatility than appreciatory shocks, jointly with conditional mean nonlinearity of smooth transition between regimes which suggests a simple trading strategy capable of generating positive profit over the sample period. However, support is also found for a competing variance in mean model consistent with a time varying risk premium that is able to rationalize the presence of unexploited profit opportunities, particularly over the latter half of the sample.

An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets

This article examines the relationship between returns and trading volume for three petroleum futures contracts. Using daily data on futures prices and trading volume, the study first tests for linear causality between returns and volume. The results of this linear causality test show that futures returns and volume have no predictive power for one another. However, because the distribution of the returns and volume series provides some evidence of nonlinear dependence, the study formally tests for and finds evidence of significant nonlinearities in the returns and volume for the three petroleum futures contracts. The returns and volume series are then filtered for linear dependence through the use of a VAR process. A nonparametric test statistic based on the correlation inWe thank Andrew C. Szakmary for supplying the data used in this study. We also thank Deborah R. Kaplan and John Wagster for helpful comments and editorial assistance.